Natural language processing review and override based on cognitive system analysis

ABSTRACT

Unstructured data are processed in response to a request for information to derive attributes. The attributes include a subset of variables utilized to determine the requested information. The attributes are ranked based on a set of relevance factors for determining the requested information. One or more attributes are selected for presentation to a user based on the ranking to receive modifications and produce modified attributes. The requested information is determined based on the set of variables including the modified attributes.

BACKGROUND 1. Technical Field

Present invention embodiments relate to computational linguistics, andmore specifically, to prioritizing items for human review in a naturallanguage processing environment using machine-implemented cognitiveprocessing.

2. Discussion of the Related Art

Natural language processing (NLP) is an active field of research anddevelopment in computer science and engineering. NLP applies acombination of techniques implemented on a computing platform, i.e.,processing hardware and software executing thereon, to extract andanalyze concepts from, among other things, unstructured text documents.A key objective of NLP is to produce information that is meaningful to ahuman user from the unstructured text. Current NLP methods do this verywell, albeit with some shortcomings in accuracy. However, even with lessthan ideal accuracy, modern NLP systems can provide suitable results.

NLP accuracy is highly dependent on whether the processor has sufficientinformation to choose the correct answer from different candidates, eachbeing formed using different interpretations of the data and of thequestion. It is not uncommon for a single NLP error on a key piece ofinformation to cause unreliable conclusions. Typical NLP systems providetools with which a user can examine NLP-based conclusions and correcterroneous and/or missing items. Current NLP correction tools usuallypresent the user with suspect items in various forms of a list. Suchmechanisms entail the user to walk through the list to verify and/orcorrect the suspect items, all the while being unaware that obtainingthe answer to its question may be achieved by correcting only a few keyitems. Since the key items may be buried anywhere in the list, the useris forced to verify and/or correct all of the items in the list toensure that those key items are correct. This can take considerabletime—so much so that some users will give up in frustration. Indeed, forcomplex systems involving a vast number of information items, such as ina healthcare system that diagnoses symptoms and suggests treatments, theNLP review process may require reviewing numerous display screens ofdata. Indeed, reviewing and correcting just one patient's full medicalhistory can drive a user to such frustration that they decide to foregothe NLP correction process altogether. The unfortunate consequence ofthis is that very few corrections are made to NLP-generated informationand confidence in the decisions made by the system suffers.

SUMMARY

According to one embodiment of the present invention, unstructured dataare processed in response to a request for information to deriveattributes. The attributes include a subset of variables utilized todetermine the requested information. The attributes are ranked based ona set of relevance factors for determining the requested information.One or more attributes are selected for presentation to a user based onthe ranking to receive modifications and produce modified attributes.The requested information is determined based on the set of variablesincluding the modified attributes.

BRIEF DESCRIPTION OF THE DRAWINGS

Generally, like reference numerals in the various figures are utilizedto designate like components.

FIG. 1 illustrates an example environment in which the present inventioncan be embodied.

FIG. 2 is a schematic block diagram of an example NLP processor by whichthe present invention can be embodied.

FIG. 3 is a conceptual block diagram of a cognitive pipeline by whichthe present invention can be embodied.

FIG. 4 is a schematic block diagram of an example user interfaceprocessing component that may be used in embodiments of the invention.

FIG. 5 is a flow diagram of an NLP override process by which the presentinvention can be embodied.

DETAILED DESCRIPTION

Embodiments of the invention provide techniques by which a user is madeaware of the key NLP-generated concepts that are suspect. Suchmechanisms prioritize what information items a user should examine for asingle scenario while, at the same time, items that are unimportant orirrelevant to the particular scenario are excluded from the reviewprocess and may be omitted from the review user interface (UI). Userpreferences as to what NLP-generated concepts are to be presented forreview can be applied in embodiments of the invention as well. Byreducing the amount of NLP-generated information that a user is asked tocorrect, the user is more likely to provide the key corrections toimportant items, and higher confidence in decisions made by the systemcan be realized.

The present invention is an improvement in natural language processing,which, by definition, is a “computer-related technology.” Theimprovements described herein allow computer performance of naturallanguage functionality not previously performed by computer-implementednatural language processing systems.

For purposes of explanation and not limitation, the domain logicexemplified herein pertains to healthcare; although it is to beunderstood that the present invention is not so limited. That is,embodiments of the invention may be used in organizational domains otherthan healthcare without departing from the spirit and intended scopethereof.

An example environment for use with present invention embodiments isillustrated in FIG. 1 as network infrastructure 10. As is illustrated,the environment includes one or more server systems 12 a-12 j,representatively referred to herein as server system(s) 12, and one ormore client or end-user systems 14 a-14 k, representatively referred toherein as client system(s) 14. Server systems 12 and client systems 14may be remote from each other and may communicate over a network 13.Network 13 may be implemented through any number of suitablecommunications media, e.g., metallic conductors, optical fiber, air,etc. using one or more signaling techniques and possibly in accordancewith one or more standardized communication protocols, e.g., InternetProtocol (IP), Transport Control Protocol (TCP), User Datagram Protocol(UDP), etc. Network 13 may be supported by suitable hardware componentsto implement wide area networks (WAN), local area networks (LAN),internets, intranets, etc. Alternatively, server systems 12 and clientsystems 14 may be sufficiently local to each other to communicatethrough direct or line-of-sight techniques, e.g., wireless radio links,fiber-less optical links, etc. In certain implementations, services andfunctionality of server systems 12 and those of client systems 14 may beperformed by common circuitry and shared computational resources in asingle device, such as a workstation.

Server systems 12 and client systems 14 may be implemented by anyconventional or other computer system preferably equipped with a displayor monitor, a base (e.g., including at least one processor (notillustrated), one or more memories (not illustrated) and/or internal orexternal network interfaces or communications devices, e.g., modem,network cards, etc. (not illustrated), optional input devices, e.g., akeyboard, mouse or other input device (not illustrated), and anycommercially available, open-source and custom software, e.g., operatingsystem, server/communications software, browser/interface software, etc.

One or more client systems 14 and/or one or more server systems 12 maybe constructed or otherwise configured to implement an informationtechnology infrastructure (ITI) 20. ITI 20 represents a unifiedframework by which enterprise data are collected, stored, retrieved andmanaged. To that end, ITI 20 may implement a structured data repository(SDR) 24, in which structured information is stored. The presentinvention is not limited to specific implementations of SDR 24; uponreview of this disclosure, those having skill in information technologywill recognize numerous structured information storage techniques thatcan be used in conjunction with the present invention without departingfrom the spirit and intended scope thereof.

ITI 20 may implement a natural language processing (NLP) component 22that, among other things, identifies entities or objects in unstructuredtext of a document, determines relationships between those entities,produces structured information data from the unstructured input textbased on the determined relationships between entities and stores thatinformation data in SDR 24. NLP component 22 may also implementcognitive computing functionality including, among other things, machinelearning, automated reasoning, human-computer interaction andquestion/answer processing.

Unstructured data repositories 30 represent various sources from whichunstructured information may be obtained. As used herein, “unstructureddata,” including the more specific “unstructured text,” refers to datathat are in some form humanly-perceivable as specific information(printed text, images, audio, etc.), but not in a form by which acomputer could identify that information (metadata, digitalrepresentations of the printed text, images, audio, etc.). Unstructureddata repositories 30 be implemented on one or more client systems 14and/or on one or more server systems 12 connected to network 13.Accordingly, data repositories 30 may be a part of ITI 20 or may beexternal to ITI 20, such as would be found as a collection of web pageson the World Wide Web. The present invention is not limited toparticular data or storage configurations for data repositories 30.

NLP component 22 may collect and process unstructured input text inaccordance with an established information model so that the processeddata may be integrated into the information scheme of ITI 20. An“information model,” as used herein, represents the linguistic conceptsfor each domain of discourse realized in ITI 20, as well as therelationships between those concepts, constraints, rules, and operationsby which textual content is construed to derive its underlyingsemantics. An information model may encompass multiple ontologies, wherean “ontology,” as used herein, is a specification of linguistic entitiesby which meaning is conveyed between agents in a domain of discourse. Anontology includes, without being limited to, the lexicography,morphologies, grammatical and syntactical rules, special patterns oftext, etc., for the domain of discourse. Ontologies may include localand/or general ontologies, which may specify general language andenterprise-internal linguistic entities, and domain-specific ontologies,which specify linguistic entities of highly-specialized domains ofdiscourse, e.g., those having idiosyncratic vocabularies and/ormorphologies, such as in the fields of healthcare, law, finance,scientific research, etc. The present invention is not limited to aspecific technique in which ontologies are realized in an informationtechnology infrastructure 20. Upon review of this disclosure, thosehaving skill in computational linguistics will recognize differenttechniques by which ontologies may be realized in particular informationintegration applications without departing from the spirit and intendedscope of the present invention.

FIG. 2 is a schematic block diagram of an example NLP component 22 bywhich the present invention can be embodied. NLP component 22 may beconstructed or otherwise configured to generate and score conclusions(answers) to user-selected lines of inquiry (questions) using naturallanguage processing, cognitive processing, machine learning and machinereasoning techniques. The combination of techniques described hereingathers and weighs evidence assembled from unstructured and structuredinformation sources and uses that evidence to express a certain level ofconfidence, as indicated by a computed confidence score, that a givenconclusion is correct. To ensure that conclusions are based onsufficient information, embodiments of the present invention identifyinconsistent and/or missing information and ranks such informationaccording to what is most important and/or most relevant to the specificline of inquiry. In so doing, human users can provide the most importantmissing/erroneous information and leave resolution of whatever othermissing information items there are for later sessions and/or for otherhuman reviewers.

As part of ongoing background operations, support documents 207 may beprovided to a linguistic processing component 205. Linguistic processingcomponent 205 may be constructed or otherwise configured to performvarious machine-executable operations for extracting information fromunstructured text in documents, e.g., support documents 207, and forproducing structured information from the document contents. Linguisticprocessing component 205 may perform standard extraction mechanisms,such as optical character recognition, metadata tagging, etc., and suchnatural language processing operations as lexical analysis, syntacticanalysis, semantic analysis, information extract-transform-loadprocessing, etc. Such processing is typically guided by a schema thatdefines the data types and data structures of which the structured dataare comprised. Those having skill in the art will recognize andunderstand various linguistic processing techniques and principles thatcan be used in conjunction with the present invention without explicitexplanation being set forth herein. Structured information produced bylinguistic component 205 may be stored in suitable data structures,referred to herein as “information objects,” of SDR 24 usingconventional and/or proprietary techniques and according to dataintegration models established by the business logic, e.g., healthcare.As used herein, information objects are considered “structured data” andcontain computer-readable data that identifies humanly-perceivableinformation.

Support documents 207 may comprise generic information common tomultiple domains of discourse, e.g., encyclopedias, dictionaries,thesauri, newswire articles, literary works, general scholastic texts,web pages, etc., as well as information that is particular to thedomains of discourse used by a specific community or enterprise. Forexample, in the field of healthcare, support documents 207 may includemedical scholastic texts, medical journal articles, diagnostic manuals,pharmacopoeia, etc. The information extracted from support documents 207may be stored in the information objects of SDR 24.

Content acquisition component 215 may be constructed or otherwiseconfigured to analyze various sample questions that would occur in aparticular problem space (e.g., healthcare) to produce information thatdescribes the kinds of questions that arise in that problem space.Cognitive acquisition component 215 may also produce informationcharacterizing the domains of discourse used in the problem space.Analyzing sample questions typically requires user involvement, i.e.,manual review and correction, while domain analysis may be performedautomatically by statistical analyses and other techniques.

The information produced by content acquisition component 215 maycomprise conclusions 260 that include the answers to the samplequestions and evidence 265 that includes context clues used in arrivingat hypotheses. This information is described in further detail below. Incertain embodiments, conclusions 260 and/or evidence 265 may be storedin common information objects of SDR 24. That is, the informationobjects produced by the components and processes of NPL component 22 mayinclude both conclusions 260 and evidence 265, as well as otherinformation described herein. When so embodied, information objects 260may contain all of the information needed for review operations in adynamically configured user interface. This feature is described indetail below.

Once sufficient content has been collected and analyzed for producingconclusions 260 and evidence 265, a set of case documents 203 may beprovided to linguistic processing component 205. Case documents 203 maybe a set of documents containing unstructured information, e.g., medicalfiles for a particular patient, related to a particular line of inquiry,e.g., diagnoses and treatment options for the particular patient giventhe information in the patient's medical files. In a healthcarescenario, case documents 205 may include results from diagnostictesting, previous and ongoing medication regimens, physician notes, etc.for the particular patient. Information objects containing thestructured information produced from case documents 203 may be providedto a cognitive processing component 220. Cognitive processing component220 may analyze the information extracted from case documents 203 andrecommend diagnoses and treatment (answers) based on the particulars ofthe case (questions) using knowledge in the relevant fields of medicineextracted by content acquisition component 215.

As illustrated in FIG. 2, cognitive processing component 220 may includea question analysis component 222 that determines the nature of thequestion being asked and that performs initial analyses for downstreamprocessing. To do so, question analysis component 222 may implement awide array of well understood techniques, such as shallow parses, deepparses, logical forms, semantic role labels, co-reference, relations,named entities, etc., as well as specific kinds of analysis foranswering questions. These operations produce the data on which otherprocessing operations are brought to bear.

Question analysis component 222 may employ question classification thatidentifies question types or parts of questions that require specialprocessing. This can include anything from single words with potentiallydouble meanings to entire clauses that have certain syntactic, semantic,or rhetorical characteristics that are handled by downstream componentsof NLP component 22. Question classes may include puzzle questions, mathquestions, definition questions, and so on. In the field of healthcare,question classes may include diagnosis questions, treatment questions,disease progression questions, etc.

Question analysis component 22 may identify a word or noun phrase in thequestion that specifies the information type (e.g., treatment answer,diagnosis answer, etc.) of the question's answer, referred to herein asa lexical answer type (LAT). Candidate answers can be scored by aconfidence metric as to whether a candidate answer considered aninstance of the LAT and making such determination is a common source ofcritical errors.

Question analysis component 22 may also identify a “focus” of thequestion, i.e., the part of the question that, if replaced by theanswer, makes the question a stand-alone statement. For example, thefocus of “the patient should do X to avoid side effects . . . ” is “doX.” The focus often (but not always) contains useful information aboutthe answer and is used in embodiments of the invention for gatheringevidence about a candidate answer.

Most questions contain relations, whether they are syntacticsubject-verb-object predicates or semantic relationships betweenentities. Cognitive processing component 220 may use relation detectionthroughout its processes, from focus and LAT determination, to passageand answer scoring. Detected relations may also be used to query atriplestore (containing subject-predicate-object information objects)from which candidate answers can be directly retrieved.

Certain questions are better answered through decomposition. Questionanalysis component 222 may use rule-based deep parsing and statisticalclassification methods to both recognize whether questions should bedecomposed and to determine how best to break them up intosub-questions. It is generally accepted by skilled artisans that thecorrect question interpretation and derived answer(s) will score higherafter all the collected evidence has been considered and all therelevant algorithms applied. Even if the question did not need to bedecomposed to determine an answer, question decomposition may improvethe confidence in the overall question in some cases.

As illustrated in FIG. 2, cognitive processing component 220 may includea hypothesis generation component 224, by which the results generated byquestion analysis component 222 are used to produce candidate answers.To do so, hypothesis generation component 224 may search variousinformation sources and extract answer-sized snippets from the searchresults. Each candidate answer plugged back into the question isconsidered a hypothesis, which NLP component 22 assesses as an answer tothe question.

Hypothesis generation component 224 may conduct a search over manycontent sources to find as much potentially answer-bearing content aspossible based on the results of question analysis. Hypothesisgeneration component 224 may realize a variety of search techniques,including the use of multiple text search engines with differentunderlying approaches, document search as well as passage search,knowledge base search, the generation of multiple search queries for asingle question and others. At this hypothesis generation phase, recall(the fraction of relevant instances that are retrieved) is significantlyfavored over precision (the fraction of retrieved instances that arerelevant) with the expectation that subsequent processing will arrive atthe correct answer, even if the set of candidates is quite large. NLPcomponent 22 thus tolerates non-relevant answer noise in the earlyprocessing stages and drives up precision downstream.

The kind of search performed by hypothesis generation component 224,e.g., document search, knowledge base search, etc., is associated with acorresponding technique for generating candidate answers. For example,for document search results from “title-oriented”resources, the titlemay be extracted as a candidate answer. Hypothesis generation component224 may generate a number of candidate answer variants from the sametitle based on substring analysis or link analysis (if the underlyingsource contains hyperlinks). Passage search results require moredetailed analysis of the passage text to identify candidate answers. Asone example of such analysis, named entity detection may be used toextract candidate answers from the passage. Some sources, such as atriplestore and reverse dictionary lookup tables, produce candidateanswers directly as their search result. Embodiments of the inventiongenerate several hundred candidate answers at this stage.

Cognitive processing component 220 may include a soft filteringcomponent 226 that applies lightweight (less resource intensive) scoringtechniques to a larger set of initial candidates to prune them down to asmaller set of candidates before the more intensive scoring isundertaken. For example, a lightweight scorer may compute the likelihoodof a candidate answer being an instance of the LAT, as discussed above.Soft filtering component 226 combines the lightweight analysis scoresinto a soft filtering score. For example, candidate answers that meet asoft filtering criterion, e.g., a predetermined threshold, proceed tohypothesis and evidence scoring component 228, while those candidatesthat do not meet the filtering criterion are routed directly tosynthesis component 232. Embodiments of the invention determine a softfiltering scoring model and filtering criterion by, for example, machinelearning over suitable training data. Certain embodiments allow roughly100 candidates pass the soft filter, but this may be a user-selectableparameter.

In hypothesis/evidence scoring component 228, candidate answers thatmeet the soft filtering criterion undergo a rigorous evaluation processthat involves gathering additional supporting evidence for eachcandidate answer, or hypothesis, and applying a wide variety of deepscoring analytics to evaluate the supporting evidence. Evidence isinformation by which a candidate answer is supported or refuted. Forexample, if a candidate answer is similar in structure, context andsemantics as other usage examples gathered as evidence, there is agreater confidence in the answer being correct. If there are no (or few)usage examples that match the candidate answer, the confidence in thecandidate would be lower.

Embodiments of the invention may implement a variety ofevidence-gathering techniques. An example technique performs a passagesearch in hypothesis generation component 224 in which the candidateanswer is added as a required term in the search query derived from thequestion. This will retrieve passages that contain the candidate answerin the context of the original question terms. Supporting evidence mayalso come from other sources like triplestores. The retrieved supportingevidence may be subjected to deep (precisely targeted) evidence scoringoperations, which evaluate the candidate answer in the context of thesupporting evidence.

Hypothesis/evidence scoring component 228 performs the bulk of the deep(precisely targeted) content analysis. Its scoring algorithms determinethe degree of certainty that retrieved evidence supports the candidateanswers. NLP component 22 may support many different scoring techniquesthat consider different dimensions of the evidence and produce a scorethat corresponds to how well evidence supports a candidate answer for agiven question. Such scoring techniques may range from formalprobabilities to counts to categorical features, based on evidence fromdifferent types of sources including unstructured text, semi-structuredtext, and triplestores. The scoring techniques may consider things likethe degree of match between a passage's predicate-argument structure andthe question, passage source reliability, geospatial location, temporalrelationships, taxonomic classification, the lexical and semanticrelations the candidate is known to participate in, the candidate'scorrelation with question terms, its popularity (or obscurity), itsaliases, and so on.

Consider the question, “who was presidentially pardoned on Sep. 8,1974,” which is correctly answered, “Nixon,” which is one of thegenerated candidate answers. One of the retrieved passages used asevidence may be “Ford pardoned Nixon on Sep. 8, 1974.” An examplepassage scorer may count the number of inverse document frequency(IDF)-weighted terms in common between the question and the passage.Another passage scorer may measure the lengths of the longest similarsubsequences between the question and passage. A third type of passagescoring measures the alignment of the logical forms of the question andpassage. A logical form is a graphical abstraction of text in whichnodes are terms in the text and edges represent either grammaticalrelationships, deep semantic relationships, or both. In the exampleabove, the logical form alignment identifies Nixon as the object of thepardoning in the passage, and that the question is asking for the objectof a pardoning. Logical form alignment gives “Nixon” a good score giventhis evidence. In contrast, a candidate answer like “Ford” would receivenear identical scores to “Nixon” for term matching and passage alignmentwith this passage, but would receive a lower logical form alignmentscore.

Other types of scorers use knowledge in triplestores, simple reasoningsuch as subsumption and disjointness in type taxonomies, and geospatialand temporal reasoning. Geospatial reasoning may be used to detect thepresence or absence of spatial relations such as directionality,borders, and containment between geoentities. For example, if a questionasks for an Asian city, then spatial containment provides evidence thatBeijing is a suitable candidate, whereas Sydney is not. Similarly,geocoordinate information associated with entities may be used tocompute relative directionality (for example, California is SW ofMontana; GW Bridge is N of Lincoln Tunnel, and so on).

Temporal reasoning may be used to detect inconsistencies between datesin the evidence and those associated with a candidate answer. Forexample, the two most likely candidate answers generated for thequestion, “who took a job as a tax collector in Andalusia in 1594,” are“Thoreau” and “Cervantes.” In this case, temporal reasoning is used torule out Thoreau as he was not alive in 1594, having been born in 1817,whereas Cervantes, the correct answer, was born in 1547 and died in1616.

Cognitive processing component 220 may include a synthesis component232, by which the hundreds of hypotheses are evaluated based onpotentially hundreds of thousands of scores to identify the singlebest-supported hypothesis given the evidence and to estimate itsconfidence, i.e., the likelihood that it is correct. Since multiplecandidate answers for a question may be equivalent despite verydifferent surface forms, answer merging may be applied by synthesiscomponent 232 to avoid conflicts in ranking techniques that utilizerelative differences between candidates. Without such answer merging,ranking algorithms might compare multiple surface forms that representthe same answer in an attempt to discriminate among them. However,different surface forms are often disparately supported in the evidenceand result in radically different, though potentially complementary,scores. Embodiments of the invention apply an ensemble of matching,normalization, and co-reference resolution algorithms, by whichequivalent and related hypotheses (for example, Abraham Lincoln andHonest Abe) are identified.

As illustrated in FIG. 2, cognitive processing component 220 includes aranking/confidence component 234 to rank the hypotheses and estimateconfidence based on their merged scores. Embodiments of the inventionimplement machine-learning techniques that operate over a set oftraining questions with known answers to train a model 270 based on thescores. Certain embodiments realize a very flat model that appliesexisting ranking algorithms directly on the merged scores and use theranking score for confidence. Other embodiments implement moreintelligent ranking in which ranking and confidence estimation isseparated into two phases. In both phases, sets of scores may be groupedaccording to their domain (for example type matching, passage scoring,and so on.) and intermediate models trained using ground truths andmethods specific for that task. Using these intermediate models, thesystem produces an ensemble of intermediate scores. Embodiments of theinvention may build and train multiple models 270 by well-known and/orproprietary machine learning techniques to handle different questionclasses as certain scores that may be crucial to identifying the correctanswer for a healthcare question, for example, may not be as useful onpuzzle questions.

Example cognitive processing component 220 produces information objectscontaining conclusions (answers) to lines of inquiry (questions) and aconfidence score for each conclusion. During the cognitive processing,no sub-component of cognitive processing component 220 commits to ananswer; all components produce features and associated confidences,scoring different question and content interpretations. Theconfidence-processing in embodiments of the invention learns how tostack and combine the confidence scores, such as by machine-learningtechniques.

Those skilled in enterprise information technology will recognize andappreciate that there is typically overarching domain or business logicassociated with the natural language and cognitive processing describedabove. Such domain logic embodies the business rules and/or constraintsthat specify how data are created, displayed, stored and changed. FIG. 3is a conceptual block diagram of a cognitive pipeline 300 by which suchdomain logic can be realized in embodiments of the invention. Cognitivepipeline 300 may comprise the sub-components of cognitive processingcomponent 220, which will be alternatively referred to herein ascognitive pipeline stages 320 a-320 f or, representatively, as cognitivepipeline stage(s) 320.

As illustrated in the figure, a question 310 may be introduced intocognitive pipeline 300 for which an answer 340 is sought. Question 310may be a dynamic user-constructed database query and/or may bepreviously established line(s) of inquiry designed to generate standardand/or usual output products (answers). For example, in healthcare, suchoutput products may be medical diagnoses given patient-specific data,treatment plans for treating specific ailments or diseases given thepatient-specific data and the medical diagnoses, etc. In certainembodiments, the desired output products (diagnosis, treatment plan,etc.) may be selected by a user through a user interface, such as userinterface 345. Question 310 may be processed by cognitive pipeline 300,as described above with reference to cognitive processing component 220.

Information data 305 represents all the data that has been previouslyprocessed by, for example, ETL operations on background data,previously-generated conclusions or recommendations, evidence gathering,etc. Information data 305 may be a collection of information objects 360stored in SDR 24. As illustrated in FIG. 3, information object 360includes one or more attributes 362 a-362 m, representatively referredto herein as attributes(s) 362, and metadata 364 that is inserted intoinformation object 360 as it is transformed and/or modified by theoperations of cognitive pipeline 300. Attributes may include variables301 and values 303 assigned to the variables 301. Metadata 364 mayinclude information described above with reference to FIG. 2, as well asinformation about the operations performed by the particular cognitiveprocessing stage 320. Attributes 362 and metadata 364 may be added andor modified, such as by assigning values 303 to variables 301, asinformation objects 360 traverse cognitive pipeline 300, i.e., from onecognitive processing stage to another. It is to be understood while asingle information object 360 is illustrated in FIG. 3, information data305 may comprise millions of information objects 360 of varying size andcontaining various information.

Domain models 375 represent domain-specific knowledge by which adomain-specific question is interpreted and processed to result in adomain-specific answer. Such domain-specific knowledge may include, forexample, background data, rules, formulas, transforms, constants, etc.by which each cognitive processing stage 320 produces that stage'soutput information objects 360 from that stage's input informationobjects 360. For example, in the healthcare domain, domain models 375realize methodology by which a certain medical diagnosis is derived fromgathered data, e.g., medical history, laboratory results, physiciansnotes, etc. Each cognitive processing stage 320 may insert newattributes 362 such as in response to computing a mathematical formula,processing text, etc., in accordance with domain model 375, may assignvalues 303 to variables 301 including numbers, text, images, video,hyperlinks, etc., based on the processing functionality of the cognitiveprocessing stage 320 as defined by domain model 375. Domain models 375may be a collection stored in models 270.

Each cognitive processing stage 320 further generates metadata 364 thatis inserted into a corresponding information object 360. Among otherthings, metadata 364 specifies what and how attributes 362 are used ineach processing stage 320, determines the origin of inserted or modifiedattributes 362, identifies what new variables 301 are generated and whatvalues 303 are assigned to those variables 301, etc., based on thefunctionality of the cognitive processing stage 360 as dictated bydomain models 375. Metadata 364 may also include confidence and otherscores on attributes 362, how important/relevant an attribute 362 is forfulfilling the domain model specifications as they pertain to question310, and so on. Each processing stage 320 may also identify missing dataas well as how important/relevant that missing data is for fulfillingthe domain model specifications and indications of such may be stored inmetadata 364.

As discussed above, there are instances when conclusions cannot bereached to a desired level of confidence due to missing or erroneousdata. Embodiments of the present invention provide techniques by which auser can override the cognitive processing to fill in missing data,correct errors, resolve ambiguities, etc., in a manner by which the mostimportant/relevant suspect information is presented more prominently (orexclusively) over less important/relevant information.

The metadata 364 generated at each cognitive processing stage 320 arecarried through remaining cognitive processing stages 320 of cognitivepipeline 300 and are then analyzed by cognitive system analysiscomponent 240. As will be described in more detail below, cognitivesystem analysis component 240 identifies missing, erroneous andambiguous attributes based on the metadata 364 generated at eachprocessing stage 320. This feature may be conceptualized as metadata 364of each cognitive processing stage 320 being provided directly tocognitive system analysis component 240, as illustrated by virtualinformation paths 325. Cognitive system analysis component 240determines from various metadata 364 what issues are raised, if any, inthe generation of answer 340 by cognitive pipeline 300.

In the example of FIG. 3, a user may review and make corrections toattributes presented on user interface 345, which may be conveyedthrough cognitive system analyzer component 240 back to the cognitiveprocessing stage 320 at which the attributes are used. Cognitivepipeline 300 uses the modified attributes to recompute answer 340. Theuser may modify any and all of the attributes identified as beingimportant/relevant to answer 340 and, optionally, may modify and/orreview other attributes that are not necessarily important/relevant asthe user desires.

Returning now to FIG. 2, cognitive system analysis component 240 may beconstructed or otherwise configured to append metadata and priority datato information objects 360 that specify what items are most important toreview, correct or confirm. In so doing, NLP component 22 produces highquality answers and affords an improved user experience by reducing thenumber of items to review and/or by suggesting acceptable or probablereplacement information to the user.

It is to be understood that while cognitive system analysis component240 is illustrated in FIG. 2 as being a separate component in NLPcomponent 22, such is for purposes of explanation and not limitation.Indeed, in certain embodiments, the functionality of the variouscomponents of cognitive system analysis component 240, as will bedescribed below, is integrated into and cooperating with othercomponents of NLP component 22.

Certain attributes derived from NLP processing component 22 (e.g., apatient's diagnostic lab results), are very critical to arriving at acorrect answer to a question (e.g., the patients diagnoses and treatmentoptions). When a patient's diagnosis is overridden, then the new updatedvalue for, e.g., diagnosis, will be used in all future answers, e.g.,treatment.

Cognitive system analysis component 240 may comprise various processingelements for tagging information objects 360 with the applicablemetadata. In one embodiment, as illustrated in FIG. 2, cognitive systemanalysis component 240 includes a variable weighting component 242, acognitive path analysis component 244, a confidence analysis component246 and a weight processing component 248. Each of these componentsanalyzes various operations performed in cognitive pipeline 300 againsta particular domain model 375 to assess what information is missing orincorrect for purposes of prioritizing most important/relevantinformation over less important/relevant information. Upon review ofthis disclosure, those skilled in the relevant arts will recognize otheranalyses that may be performed by cognitive system analysis component240 without departing from the spirit and intended scope of the presentinvention.

Cognitive processing component 220 analyzes data and makes conclusionsor recommendations for that data through applicable domain models 375based on one or more values assigned to respective attributes 362. Thisis typically a small subset of the full set of items analyzed by NLPprocessing component 22. Different attributes 362 may have differentimportance or weight in deriving answer 340 to question 310 than otherattributes 362. Some variables are essential, while other variables arenot essential in achieving a high quality answer 340, but maynevertheless be used in a particular domain model 375. Variableweighting component 242 may assign a weight to the variables indicatingthe importance, relevance and/or significance the corresponding variableis in producing a reasonably accurate answer 340.

To illustrate, consider a set of variables, e.g., Histology, Stage, Lineof Therapy, Epidermal Growth Factor Receptor (EGFR) Mutation,Recurrence, etc., and assume that these attributes are necessary toevaluate a lung cancer patient. In this case, variable weightingcomponent 242 may apply suitable weights to these attributes indicatingthat they are important/relevant. For a leukemia patient, which uses aslightly different set of variables, some of which, such as Recurrence,must be known (per the domain model 375), while other variables, such asRET Mutation status, need not be known to any specific accuracy and mayeven be missing. As explained above, metadata 364 may specify exactlywhich inputs (attributes 362 in information objects 360) are required tomake good recommendations for a particular line of inquiry, andcognitive system analyzing component 240 can prioritize those inputs forreview and/or correction. Variable weighting component 242 may assignweights on these attributes 362 indicating how important/relevantparticular attributes are in determining answer 340 for a topic ofinquiry, e.g., a particular type of cancer. Thereby, variable weightingcomponent 242 can distinguish high priority items, i.e., items that mustbe known for a certain diagnosis, from low priority items, i.e., itemsthat need not be known for the diagnosis, which can save the user timesince the low priority items need not be reviewed to get a high qualitytherapy recommendation.

Cognitive processing paths through cognitive pipeline 300 may dictatewhat data is relevant or even critical in obtaining a recommendation fora given scenario. In certain embodiments, a domain model 375 used incognitive pipeline 300 is developed around a known set of variables(e.g., Histology, Stage, etc.). The importance of each variable in theset can change depending on the question/answer scenario. For thehealthcare example, cognitive pipeline 300 can determine that for thatone patient, certain pieces of information are of greater importancethan other information. For example, if an oncology patient exhibitsevidence of metastatic disease, cognitive pipeline 300 may not besensitive to attributes that are relevant for surgical recommendations,like pulmonary function test (PFT) values. That is, since surgery maynot be a treatment option for advanced stage/metastatic cases, PFTvalues are not relevant to recommendations that would typically besought for metastatic cancer. Thus, in certain embodiments of theinvention, cognitive path analysis component 244 can consider theattributes for a particular case and refine the priority of NLP datacorrection based on that case. To do so, cognitive path analysiscomponent 244 may assign another weight to the attributes that indicateits importance/relevance in the cognitive processing scenario at hand.

Confidence (or lack of confidence) in an answer can be related toconfidence in certain NLP-derived attributes, and the user can beinformed of this confidence as they consider the answer 340. Confidenceanalysis component 246 may evaluate answer 340 to question 310, e.g.,treatment options for a lung cancer patient, and indicate a level ofconfidence in answer 340. For example, when all of the inputs requiredto produce an answer have been provided to cognitive processingcomponent 220, confidence analysis component 246 may indicate a highlevel of confidence in answer 340. On the other hand, cognitiveprocessing component 220 may determine that one or more important piecesof data are missing and may thus indicate low confidence in answer 340.Embodiments of the present invention allow cognitive processingcomponent 220 to identify which pieces of data are required to providean answer, and whether it is confident in those pieces of data. When theuser reads the answer, such as on user interface component 345, thisconfidence information may be presented to the user along with theanswer 340. In this way, the user can see the level of confidence in theanswer, and what missing pieces of information (if any) would helpimprove the answer.

Weight processing component 248 identifies important/relevantinformation as well as routine/unimportant information and appliesoverall weights to the various NLP-derived items based on informationprovided by variable weighting component 242, cognitive path analysiscomponent 244 and confidence analysis component 246. Weight processingcomponent 248 may determine an overall weight or rank indicating theimportance in presenting that item to the user for review. In certainembodiments, the overall weight is compared against a threshold value todetermine which items should be presented to a user for NLPreview/correction. In the example provided in Table 1 below, thethreshold for the overall weight may have been tuned to be at 70% (asmarked in Table 1). As illustrated in the example below, only theoverall weight of the Recurrence item meets the threshold condition(greater than 70%) and thus is the only item required to be prominentlypresented to the user.

TABLE 1 Weight of importance Weight of based on cognitive OncologicalImportance to processing path for Concept Cognitive Processing thisparticular patient Overall weight Recurrence 100% 100% (could be due100%  to no value being present, or could be due to low confidence onpart of NLP processing that value is accurate.) THRESHOLD = 70%Histology 100% 65% 65% Stage 100% 20% 16% Line of Therapy 70% 50% 3.5% EGFR Mutation 30% 50% 1.5%  Surgery History 100% 0%  0% Smoking History5% 0%  0% . . . Other concepts will . . . . . . . . . also be ranked . ..

In certain embodiments, high priority attributes may be presented to theuser, who might also be given an option to view additional NLP-derivedattributes. If it is determined that no high priority NLP-derivedattributes need to be presented for correction (that is, the confidenceis high in that all of the data necessary to provide a high qualityanswer has been provided), then the user would not be prompted for anyNLP corrections. However, when the option to view additional attributesis implemented, the user would still have a way to review less importantNLP data and drill into it. For example, in certain embodiments,attributes may be presented in an ordered list that can be sorted bypriority or weight. However, it is to be understood that embodiments ofthe invention are intended to reduce the burden on the user incorrecting NLP data by only forcing the user to review the mostimportant NLP data.

In the example above, a threshold value of 70% is set as the cutoff ofhigh priority NLP items to present for cognitive processing override.The threshold can be selected in different ways, including by performingan initial user study to evaluate what threshold is suitable, byenabling the processing to observe whether users tend to drill down intoNLP items that aren't indicated as high priority items (which mayindicate that the threshold needs to be lowered) and/or by allowing theuser to manually configure the threshold so as to specify how much NLPoverride information they want to see.

FIG. 4 is a schematic block diagram of an example UI processingcomponent 250 that may be used in embodiments of the invention. Incertain embodiments, UI processing component 250 rendersdynamically-configured UI components on a display/human interfacedevices component 255. In conventional NLP implementations, the UIpresentation of derived attributes is static, i.e., fixed by design andhard-coded into the UI rendering mechanisms. In such implementations, UIcomponents that allow users to review derived attributes may be noisy toindividual users, i.e., displaying information for review that isrelatively irrelevant to or otherwise noninfluential on the conclusionsof a particular user's line of inquiry. When relevant derived attributesor problematic NLP-produced items are not prominently displayed andeasily accessible for review and edit, the user may become overwhelmedwith data and it is less likely that the user will see those attributesthat greatly influence the conclusions on which that user relies.Additionally, different users may have different preferences for howmuch data they want to see in a particular UI environment. Some may wanta detailed view to review all available information while others maywant to see less data, e.g., the items most critical to an accurateconclusion. Other users may only be interested in particular attributes,e.g., a pathologist who wants to verify that certain concepts related totheir own patient's pathology reports were correctly identified.Embodiments of the invention afford such customization.

As illustrated in FIG. 4, UI rendering component 410 may collectinformation and related metadata from SDR 24 for correspondinginformation objects 360 that are to be displayed to a user, such as byan interface component 420. UI rendering component 410 may also obtainconstraints 415 on the collected information that define how theinformational objects are rendered in UI component 420. Constraints 415may include data type, e.g., integer, floating-point number, characterstring, etc., and ranges on the data, e.g., integer between 0 and 3,character string of 15 characters, etc. Such information may bedetermined from stored data structures specifying the schema by whichinformation is characterized by NLP as well as from analysis on valuesassigned to informational attributes as natural language processing isperformed, e.g., tracking per-attribute values duringextract-transform-load (ETL) operations. Other constraints may beestablished through explicit formatting rules, such as to ensure thatinformation is displayed neatly and within size limits of UI component420. More complex constraints and even relationships among attributescould be specified, such as “Therapy end date must be later than Therapystart date.” This encapsulation may also include references torepresentational state transfer (REST) application programminginterfaces (APIs), such as “Use REST API xyz to get a list of possiblevalues for this attribute.” Those having skill in the relevant arts willrecognize other constraints that can be implemented in conjunction withembodiments of the present invention. Additionally, while constraints415 is illustrated in FIG. 4 as being contained in a storage componentseparate from SDR 24, it is to be understood that constraints 415 may beimplemented as metadata that is carried in information objects 360stored in SDR 24.

UI rendering component 410 may additionally access user profiles 413 toobtain user-specified preferences defining what information is renderedin UI component 420. For example, a user profile 413 may specify whatderived attributes to render in UI component 420. Another user profile413 may specify particular lines of inquiry, e.g., diagnoses andtreatment options for particular cancers, and UI rendering component 410may collect the derived attributes relevant to or otherwise associatedwith those lines of inquiry. Yet another user profile 413 may specify aconfidence threshold that a conclusion and/or information used informing a conclusion must meet; those items for which the thresholdconfidence is not met may be flagged for review. Embodiments of thepresent invention may implement fine control over what informationalobjects may be rendered and ultimately displayed to a user essentiallywithout limit insofar as user preferences can be specified for andapplied to machine-implemented UI rendering and display techniques.

UI rendering component 410 may generate UI component 420 to presentinformation objects in accordance with user profiles 413 and constraints415. UI component 420 may include static information panels,representatively illustrated at static information panel 422, andmutable information panels, representatively illustrated at mutableinformation panel 424. Static information panels 422 may have renderedtherein information objects that are constant, such as patientinformation, diagnoses and treatment dates, etc. Mutable informationpanels 424 may have rendered therein information objects that can bemodified by a user. For example, such information objects may includederived attribute values that were produced by NLP techniques describedherein and that may be overridden by the user. In the embodimentillustrated in FIG. 4, a user may select an information objectrepresentation for review, representatively illustrated at informationobject representation 426, by HID activation 430, such as by mouseclick. Information object representation 426 may be logically linked toor otherwise associated with processor-executable instructions thatinstantiates and renders an override control 435 in response to HIDactivation 430. Override control 435 may be populated with details ofand relevant to the information object underlying information objectrepresentation 426 in a manner by which the associated values can bemodified. Override control 435 may include various well-known windowcontrols (not illustrated) including edit controls, drop-down listcontrols, buttons, static labels, etc. In one embodiment, multi-itemcontrols such as drop-down list boxes, may be populated by informationcontained in constraints 415. For example, in the case of integer-valueditems, a drop-down list box may list all integer values for the itemspecified in constraints 415.

Using such controls in override control 435, a user may edit orotherwise modify one or more values, representatively illustrated atuser edit 440, assigned to the associated information objectrepresentation 426. The modified value may be stored in thecorresponding information object of SDR 24 and new conclusions may begenerated, as described with reference to FIG. 2, based on the newvalues. When information objects are prioritized in UI component 420, asdescribed above, the user may perform NLP overrides without having tosearch through irrelevant or unimportant information objects for theinformation that pertains to the user's line of inquiry.

The techniques discussed above also simplify modifications to UIcomponents when the information that is to be displayed changes. Thatis, adding or removing derived attributes or other information objectsis expensive when those attributes are hard-coded in the UI logic, as isthe case for conventional override techniques. By way of the techniquesdescribed above, the UI logic is relatively independent from the NLPlogic; as long as the information is stored in SDR 24 in accordance withthe overarching information schema, a UI component 420 can be generatedby embodiments of the present invention in accordance with user profiles413 and constraints 415.

In certain embodiments, NLP component 22 may be based on theUnstructured Information Management Architecture (UIMA) maintained bythe Organization for the Advancement of Structured Information Standards(OASIS). UIMA was designed to support interoperability and scaleout oftext and multimodal analysis applications. All of the components incognitive NLP component 22 may be implemented as UIMA annotators, whichare processor-executable components that analyze text and produceannotations or assertions about the text. It is to be understood thatNLP architectures other than UIMA, including those of proprietarydesign, may be used in conjunction with the present invention withoutdeparting from the spirit and intended scope thereof.

FIG. 5 is a flow diagram of an NLP override process 500 by which thepresent invention can be embodied. It is to be understood that the flowdiagram in FIG. 5 is constructed to aid in the understanding of thepresent invention and not for efficient implementation of NLP overrideprocess 500. As such, the illustrated flow diagram represents a singlepass, single thread implementation whereas a more efficient realizationcomprises multiple iterations of the illustrated single pass processexecuting in multiple execution threads. Those having skill in therelevant arts will recognize multiple programming paradigms throughwhich the techniques described herein may be realized for machineexecution.

In operation 505 of process 500, NLP operations are performed togenerate structured information from unstructured information. Process500 transitions to operation 510 by which one or more conclusions torespective lines of inquiry are generated from the structuredinformation by cognitive processing. Metadata are also generated duringcognitive processing indicating the importance/relevance of theattributes involved in generating the conclusion. In operation 515,weights for the attributes are computed. In operation 525, it isdetermined whether any of the weights on the attributes meet apredetermined threshold criterion and the weights meeting the thresholdcriterion are flagged for review in operation 530.

In operation 540, information objects are collected for review includingthe flagged attribute. The collected information objects may includethose involved in forming the conclusion as prioritized by cognitiveprocessing and/or user preferences. In operation 545, a UI componentcontaining the collected and prioritized information objects is renderedor otherwise generated according to the user profile and UI constraintsand the rendered UI component is displayed to the user. Process 500 maytransition to operation 550, by which it is determined whether the userhas activated an information object for purposes of overriding theflagged conclusion. In response to affirming the user activation,process 500 transitions to operation 555, by which an override UIcomponent is generated and displayed to include those informationobjects relevant to the selected information object for purposes ofmodifying the information. In operation 560, it is determined whether anattribute contained in an information object is modified through theoverride UI component and, upon affirming such modification, process 500transitions to operation 565, by which new conclusions to the user'sline(s) of inquiry are generated using the modified information object.Process 500 may then terminate.

Client systems 14 enable users to submit documents (e.g., case documents203 and supporting documents 207, etc.) to server systems 12. The serversystems include an NLP component 22 to process unstructured informationinto structured information and to generate conclusions based on auser's line of inquiry. A database system, e.g., SDR 24, may storevarious information for the analysis (e.g., information objects,conclusions, evidence, models, etc.). The database system may beimplemented by any conventional or other database or storage unit, maybe local to or remote from server systems 12 and client systems 14, andmay communicate via any appropriate communication medium (e.g., localarea network (LAN), wide area network (WAN), Internet, hardwire,wireless link, Intranet, etc.). The client systems may present agraphical user (e.g., GUI, etc.) or other interface (e.g., command lineprompts, menu screens, etc.) to solicit information from userspertaining to analyses and user lines of inquiry, and may providereports including analysis results (e.g., text analytics,missing/erroneous information, conclusions, etc.).

One or more client systems 14 may analyze documents to produceNLP-derived conclusions to lines of inquiry when operating as astand-alone unit. In a stand-alone mode of operation, the client systemstores or has access to the data (e.g., information objects,conclusions, evidence, models, etc.), and includes an NLP component toprocess unstructured information into structured information and togenerate conclusions based on a user's line of inquiry. The graphicaluser (e.g., GUI, etc.) or other interface (e.g., command line prompts,menu screens, etc.) solicits information from a corresponding userpertaining to the desired documents and analysis, and may providereports including analysis results.

The NLP component may include one or more modules or units to performthe various functions of present invention embodiments described above.The various components (e.g., cognitive processing component, reviewprocessing component, etc.) may be implemented by any combination of anyquantity of software and/or hardware modules or units, and may residewithin memory of the server and/or client systems for execution by aprocessor.

It will be appreciated that the embodiments described above andillustrated in the drawings represent only a few of the many ways ofimplementing embodiments for NLP review and override based on cognitivesystem analysis.

The environment of the present invention embodiments may include anynumber of computer or other processing systems (e.g., client or end-usersystems, server systems, etc.) and databases or other repositoriesarranged in any desired fashion, where the present invention embodimentsmay be applied to any desired type of computing environment (e.g., cloudcomputing, client-server, network computing, mainframe, stand-alonesystems, etc.). The computer or other processing systems employed by thepresent invention embodiments may be implemented by any number of anypersonal or other type of computer or processing system (e.g., desktop,laptop, PDA, mobile devices, etc.), and may include any commerciallyavailable operating system and any combination of commercially availableand custom software (e.g., browser software, communications software,server software, NLP processing module, etc.). These systems may includeany types of monitors and input devices (e.g., keyboard, mouse, voicerecognition, etc.) to enter and/or view information.

It is to be understood that the software (e.g., NLP, cognitiveprocessing, review processing) of the present invention embodiments maybe implemented in any desired computer language and could be developedby one of ordinary skill in the computer arts based on the functionaldescriptions contained in the specification and flow charts illustratedin the drawings. Further, any references herein of software performingvarious functions generally refer to computer systems or processorsperforming those functions under software control. The computer systemsof the present invention embodiments may alternatively be implemented byany type of hardware and/or other processing circuitry.

The various functions of the computer or other processing systems may bedistributed in any manner among any number of software and/or hardwaremodules or units, processing or computer systems and/or circuitry, wherethe computer or processing systems may be disposed locally or remotelyof each other and communicate via any suitable communications medium(e.g., LAN, WAN, Intranet, Internet, hardwire, modem connection,wireless, etc.). For example, the functions of the present inventionembodiments may be distributed in any manner among the variousend-user/client and server systems, and/or any other intermediaryprocessing devices. The software and/or algorithms described above andillustrated in the flow charts may be modified in any manner thataccomplishes the functions described herein. In addition, the functionsin the flow charts or description may be performed in any order thataccomplishes a desired operation.

The software of the present invention embodiments (e.g., NLP, cognitiveprocessing, review processing) may be available on a non-transitorycomputer useable medium (e.g., magnetic or optical mediums,magneto-optic mediums, floppy diskettes, CD-ROM, DVD, memory devices,etc.) of a stationary or portable program product apparatus or devicefor use with stand-alone systems or systems connected by a network orother communications medium.

The communication network may be implemented by any number of any typeof communications network (e.g., LAN, WAN, Internet, Intranet, VPN,etc.). The computer or other processing systems of the present inventionembodiments may include any conventional or other communications devicesto communicate over the network via any conventional or other protocols.The computer or other processing systems may utilize any type ofconnection (e.g., wired, wireless, etc.) for access to the network.Local communication media may be implemented by any suitablecommunication media (e.g., local area network (LAN), hardwire, wirelesslink, Intranet, etc.).

The system may employ any number of any conventional or other databases,data stores or storage structures (e.g., files, databases, datastructures, data or other repositories, etc.) to store information(e.g., information objects, conclusions, evidence, models, etc.). Thedatabase system may be implemented by any number of any conventional orother databases, data stores or storage structures (e.g., files,databases, data structures, data or other repositories, etc.) to storeinformation (e.g., information objects, conclusions, evidence, models,etc.). The database system may be included within or coupled to theserver and/or client systems. The database systems and/or storagestructures may be remote from or local to the computer or otherprocessing systems, and may store any desired data (e.g., informationobjects, conclusions, evidence, models, etc.).

The present invention embodiments may employ any number of any type ofuser interface (e.g., Graphical User Interface (GUI), command-line,prompt, etc.) for obtaining or providing information (e.g., conclusions,review and override data, etc.), where the interface may include anyinformation arranged in any fashion. The interface may include anynumber of any types of input or actuation mechanisms (e.g., buttons,icons, fields, boxes, links, etc.) disposed at any locations toenter/display information and initiate desired actions via any suitableinput devices (e.g., mouse, keyboard, etc.). The interface screens mayinclude any suitable actuators (e.g., links, tabs, etc.) to navigatebetween the screens in any fashion.

The report may include any information arranged in any fashion, and maybe configurable based on rules or other criteria (e.g., constraints,user profiles, etc.) to provide desired information to a user (e.g.,text analytics, conclusions, review/correction data, etc.).

The present invention embodiments are not limited to the specific tasksor algorithms described above, but may be utilized for other domains,such as finances, legal analysis, etc.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”,“comprising”, “includes”, “including”, “has”, “have”, “having”, “with”and the like, when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

What is claimed is:
 1. A method of processing a request for information comprising: processing unstructured data in response to the request for information to derive attributes, wherein the attributes comprise a subset of variables utilized to determine the requested information; ranking the attributes for determining the requested information based on a set of relevance factors; selecting one or more attributes based on the ranking for presentation to a user to receive modifications and produce modified attributes; and determining the requested information based on the set of variables including the modified attributes.
 2. The method of claim 1, wherein the set of relevance factors includes one or more selected from a group of: a relevance of an attribute with respect to determining the requested information; a degree the attribute affects the requested information; an importance of the attribute with respect to the request; and a confidence for the attribute with respect to determining the requested information.
 3. The method of claim 1, wherein ranking the attributes comprises: assigning a weight to each of the relevance factors for an attribute and combining the weights to produce an overall weight for the attribute; and ranking the attributes based on corresponding overall weights.
 4. The method of claim 3, wherein selecting one or more attributes based on the ranking comprises: comparing the overall weight of each attribute to a threshold; and selecting each attribute satisfying the threshold.
 5. The method of claim 4, wherein the threshold is determined based on one or more selected from a group of: specified by a user; and history of user viewing of individual ones of the attributes.
 6. The method of claim 1, further comprising: presenting the requested information and corresponding confidence data, wherein the corresponding confidence data indicates a sufficiency of information to determine the requested information and desired information to improve results for the requested information.
 7. The method of claim 1, wherein the processing of the unstructured data includes natural language processing.
 8. A system for processing a request for information comprising: a processor configured to: process unstructured data in response to the request for information to derive attributes, wherein the attributes comprise a subset of variables utilized to determine the requested information; rank the attributes for determining the requested information based on a set of relevance factors; select one or more attributes based on the ranking for presentation to a user to receive modifications and produce modified attributes; and determine the requested information based on the set of variables including the modified attributes.
 9. The system of claim 8, wherein the set of relevance factors includes one or more selected from a group of: a relevance of an attribute with respect to determining the requested information; a degree the attribute affects the requested information; an importance of the attribute with respect to the request; and a confidence for the attribute with respect to determining the requested information.
 10. The system of claim 8, wherein ranking the attributes comprises: assigning a weight to each of the relevance factors for an attribute and combining the weights to produce an overall weight for the attribute; and ranking the attributes based on corresponding overall weights.
 11. The system of claim 10, wherein selecting one or more attributes based on the ranking comprises: comparing the overall weight of each attribute to a threshold; and selecting each attribute satisfying the threshold.
 12. The system of claim 11, wherein the threshold is determined based on one or more selected from a group of: specified by a user; and history of user viewing of individual ones of the attributes.
 13. The system of claim 8, wherein the processor is further configured to: present the requested information and corresponding confidence data, wherein the corresponding confidence data indicates a sufficiency of information to determine the requested information and desired information to improve results for the requested information.
 14. A computer program product for processing a request for information, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to: process unstructured data in response to the request for information to derive attributes, wherein the attributes comprise a subset of variables utilized to determine the requested information; rank the attributes for determining the requested information based on a set of relevance factors; select one or more attributes based on the ranking for presentation to a user to receive modifications and produce modified attributes; and determine the requested information based on the set of variables including the modified attributes.
 15. The computer program product of claim 14, wherein the program instructions include further program instructions executable by the processor to cause the processor to: include, in the set of relevance factors, one or more selected from a group of: a relevance of an attribute with respect to determining the requested information; a degree the attribute affects the requested information; an importance of the attribute with respect to the request; and a confidence for the attribute with respect to determining the requested information.
 16. The computer program product of claim 14, wherein the program instructions include further program instructions executable by the processor to cause the processor to: assign a weight to each of the relevance factors for an attribute and combining the weights to produce an overall weight for the attribute; and rank the attributes based on corresponding overall weights.
 17. The computer program product of claim 16, wherein the program instructions include further program instructions executable by the processor to cause the processor to: compare the overall weight of each attribute to a threshold; and select each attribute satisfying the threshold.
 18. The computer program product of claim 17, wherein the program instructions include further program instructions executable by the processor to cause the processor to: determine the threshold based on one or more selected from a group of: specified by a user; and history of user viewing of individual ones of the attributes.
 19. The computer program product of claim 14, wherein the program instructions include further program instructions executable by the processor to cause the processor to: present the requested information and corresponding confidence data, wherein the corresponding confidence data indicates a sufficiency of information to determine the requested information and desired information to improve results for the requested information. 